Manosroi Worapaka, Koetsuk Lattapol, Phinyo Phichayut, Danpanichkul Pojsakorn, Atthakomol Pichitchai
Division of Endocrinology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Faculty of Medicine, Center for Clinical Epidemiology and Clinical Statistics, Chiang Mai University, Chiang Mai, Thailand.
Front Med (Lausanne). 2023 Jan 11;9:1106312. doi: 10.3389/fmed.2022.1106312. eCollection 2022.
Prolonged length of stay (LOS) in osteoporotic femoral neck fracture patients increased the hospital care cost and demonstrated in-hospital complications. This study aimed to develop an ease-of use predictive model of prolonged LOS in osteoporotic femoral neck fracture patients. In this 5-year retrospective study, the medical charts of 255 patients admitted to hospital with an osteoporotic femoral neck fracture resulting from a simple fall from January 2014 to December 2018 were reviewed. Multivariable fractional polynomials (MFP) algorithms was applied to develop the predictive model from candidate predictors of prolonged LOS. The discrimination performance of predictive model was evaluated using the receiver operating characteristic curve (ROC). Internal validity was assessed using bootstrapping. From 289 patients who were hospitalized with an osteoporotic fracture of femoral neck throughout this study, 255 (88%) fulfilled the inclusion criteria. There was 54.90% (140 of 255 patients) of patients who had prolonged LOS. The predictors of the predictive model were age, BMI, ASA score class 3 or 4, arthroplasty and time from injury to surgery. The area under ROC curve of the model was 0.83 (95% confidence interval 0.77-0.88). Internal validation with bootstrap re-sampling revealed an optimism of -0.002 (range -0.300-0.296) with an estimated shrinkage factor of 0.907 for the predictive model. The current predictive model developed from preoperative predictors which had a good discriminative ability to differentiate between length of hospitalization less than 14 days and prolonged LOS in osteoporotic femoral neck patients. This model can be applied as ease-of use calculator application to help patients, their families and clinicians make appropriate decisions in terms of treatment planning, postoperative care program, and cost-effectiveness before patients receiving the definitive treatments.
骨质疏松性股骨颈骨折患者住院时间延长会增加医院护理成本，并出现院内并发症。本研究旨在建立一种易于使用的预测模型，以预测骨质疏松性股骨颈骨折患者住院时间延长的情况。在这项为期5年的回顾性研究中，我们回顾了2014年1月至2018年12月期间因简单跌倒导致骨质疏松性股骨颈骨折而入院的255例患者的病历。应用多变量分数多项式（MFP）算法，从住院时间延长的候选预测因素中建立预测模型。使用受试者工作特征曲线（ROC）评估预测模型的辨别性能。使用自抽样法评估内部效度。在本研究中，共有289例骨质疏松性股骨颈骨折患者住院，其中255例（88%）符合纳入标准。54.90%（255例患者中的140例）患者住院时间延长。预测模型的预测因素为年龄、体重指数、美国麻醉医师协会（ASA）评分3或4级、关节置换术以及受伤至手术的时间。该模型的ROC曲线下面积为0.83（95%置信区间0.77 - 0.88）。自抽样法的内部验证显示，预测模型的乐观偏差为-0.002（范围-0.300至0.296），估计收缩因子为0.907。目前基于术前预测因素建立的预测模型，对于区分骨质疏松性股骨颈患者住院时间少于14天和住院时间延长具有良好的辨别能力。该模型可作为易于使用的计算器应用程序，帮助患者及其家属以及临床医生在患者接受最终治疗前，在治疗计划、术后护理方案和成本效益方面做出适当决策。
